Evaluation of the quality of cold meats by computer-assisted image analysis

Applications of Electronic Nose, Electronic Eye and Electronic Tongue in Quality, Safety and Shelf Life of Meat and Meat Products: A Review

The quality and shelf life of meat and meat products are key factors that are usually evaluated by complex and laborious protocols and intricate sensory methods. Devices with attractive characteristics (fast reading, portability, and relatively low operational costs) that facilitate the measurement of meat and meat products characteristics are of great value. This review aims to provide an overview of the fundamentals of electronic nose (E-nose), eye (E-eye), and tongue (E-tongue), data preprocessing, chemometrics, the application in the evaluation of quality and shelf life of meat and meat products, and advantages and disadvantages related to these electronic systems. E-nose is the most versatile technology among all three electronic systems and comprises applications to distinguish the application of different preservation methods (chilling vs. frozen, for instance), processing conditions (especially temperature and time), detect adulteration (meat from different species), and the monitoring of shelf life. Emerging applications include the detection of pathogenic microorganisms using E-nose. E-tongue is another relevant technology to determine adulteration, processing conditions, and to monitor shelf life. Finally, E-eye has been providing accurate measuring of color evaluation and grade marbling levels in fresh meat. However, advances are necessary to obtain information that are more related to industrial conditions. Advances to include industrial scenarios (cut sorting in continuous processing, for instance) are of great value.

Prediction of food quality parameters in fish burgers by partial least square models using RGB pattern of digital images

ABSTRACT

Rancid taste, pH, and TBARS are important quality parameters of food oxidation, analyzed in a time-consuming and destructive way. Non-destructive characterization of food can be achieved correlating this data with computational vision. Thus, the present study aimed to use RGB digital images to predict sensory rancid taste, pH, and TBARS results in fish burgers. A mobile obtained the digital images, in a controlled environment, and 768 grayscales were performed using RGB histograms. The pH, showed a peak at 21st day of storage, which PCA confirmed by isolating the 21st samples, corroborated by HCA grouping 21st day samples. PLS models from RGB digital images and sensory rancidity, pH and TBARS data, using mean center method and SIMPLS algorithm found models with > 0.97 R². Thus, any digital image of this batch of burgers, inserted into the model to predict rancid taste, pH and TBARS has high confidence level of prediction.

Color approach to the analysis of white crystal cane sugar for the detection of solid impurities

BACKGROUND

Sugar is consumed worldwide and so the quality control of sugar cane is necessary. Solid impurities are an inherent part of industrial sugar processing. Dark particles and adulteration with sand must be controlled. Sixty-four samples of white crystal cane sugar analysis in the presence of both kinds of impurities (dark particles and sand) were assessed using an affordable digital image system and a multivariate calibration strategy.

RESULTS

The quality parameters for the multivariate calibration models obtained to estimate sugar content were remarkable. Color descriptors from digital images allowed identification of different levels of sugar content for the following three ranges: 0-49.99 wt%, 50.03-78.99 wt%, and 82.99-100 wt%. The multivariate model using red (R), green (G), Blue (B), and luminosity (L) color descriptors showed low standard errors of cross-validation (SECV) and validation (SEV) of 7.63 and 6.01 wt%, respectively.

CONCLUSIONS

The method is affordable and reliable, and might aid quick screening in situations where access to a laboratory or instrumentation is restricted. © 2021 Society of Chemical Industry.

Combining deep learning and fluorescence imaging to automatically identify fecal contamination on meat carcasses

Food safety and foodborne diseases are significant global public health concerns. Meat and poultry carcasses can be contaminated by pathogens like E. coli and salmonella, by contact with animal fecal matter and ingesta during slaughter and processing. Since fecal matter and ingesta can host these pathogens, detection, and excision of contaminated regions on meat surfaces is crucial. Fluorescence imaging has proven its potential for the detection of fecal residue but requires expertise to interpret. In order to be used by meat cutters without special training, automated detection is needed. This study used fluorescence imaging and deep learning algorithms to automatically detect and segment areas of fecal matter in carcass images using EfficientNet-B0 to determine which meat surface images showed fecal contamination and then U-Net to precisely segment the areas of contamination. The EfficientNet-B0 model achieved a 97.32% accuracy (precision 97.66%, recall 97.06%, specificity 97.59%, F-score 97.35%) for discriminating clean and contaminated areas on carcasses. U-Net segmented areas with fecal residue with an intersection over union (IoU) score of 89.34% (precision 92.95%, recall 95.84%, specificity 99.79%, F-score 94.37%, and AUC 99.54%). These results demonstrate that the combination of deep learning and fluorescence imaging techniques can improve food safety assurance by allowing the industry to use CSI-D fluorescence imaging to train employees in trimming carcasses as part of their Hazard Analysis Critical Control Point zero-tolerance plan.

Accurate prediction and genome-wide association analysis of digital intramuscular fat content in longissimus muscle of pigs

Intramuscular fat (IMF) content is a critical indicator of pork quality that affects directly the purchasing desire of consumers. However, to measure IMF content is both laborious and costly, preventing our understanding of its genetic determinants and improvement. In the present study, we constructed an accurate and fast image acquisition and analysis system, to extract and calculate the digital IMF content, the proportion of fat areas in the image (PFAI) of the longissimus muscle of 1709 animals from multiple pig populations. PFAI was highly significantly correlated with marbling scores (MS; 0.95, r²  = 0.90), and also with IMF contents chemically defined for 80 samples (0.79, r²  = 0.63; more accurate than direct analysis between IMF contents and MS). The processing time for one image is only 2.31 s. Genome-wide association analysis on PFAI for all 1709 animals identified 14 suggestive significant SNPs and 1 genome-wide significant SNP. On MS, we identified nine suggestive significant SNPs, and seven of them were also identified in PFAI. Furthermore, the significance (-log P) values of the seven common SNPs are higher in PFAI than in MS. Novel candidate genes of biological importance for IMF content were also discovered. Our imaging systems developed for prediction of digital IMF content is closer to IMF measured by Soxhlet extraction and slightly more accurate than MS. It can achieve fast and high-throughput IMF phenotype, which can be used in improvement of pork quality.

Emerging nondestructive approaches for meat quality and safety evaluation-A review

Meat is one of the most consumed agro-products because it contains proteins, minerals, and essential vitamins, all of which play critical roles in the human diet and health. Meat is a perishable food product because of its high moisture content, and as such there are concerns about its quality, stability, and safety. There are two widely used methods for monitoring meat quality attributes: subjective sensory evaluation and chemical/instrumentation tests. However, these methods are labor-intensive, time-consuming, and destructive. To overcome the shortfalls of these conventional approaches, several researchers have developed fast and nondestructive techniques. Recently, electronic nose (e-nose), computer vision (CV), spectroscopy, hyperspectral imaging (HSI), and multispectral imaging (MSI) technologies have been explored as nondestructive methods in meat quality and safety evaluation. However, most of the studies on the application of these novel technologies are still in the preliminary stages and are carried out in isolation, often without comprehensive information on the most suitable approach. This lack of cohesive information on the strength and shortcomings of each technique could impact their application and commercialization for the detection of important meat attributes such as pH, marbling, or microbial spoilage. Here, we provide a comprehensive review of recent nondestructive technologies (e-nose, CV, spectroscopy, HSI, and MSI), as well as their applications and limitations in the detection and evaluation of meat quality and safety issues, such as contamination, adulteration, and quality classification. A discussion is also included on the challenges and future outlooks of the respective technologies and their various applications.

Deep learning and machine vision for food processing: A survey

The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

A comprehensive insight into the effects of microbial spoilage, myoglobin autoxidation, lipid oxidation, and protein oxidation on the discoloration of rabbit meat during retail display

The effects of the retail display temperature (8 °C, 3 °C and - 1 °C) on the discoloration of the Longissimus thoracis et lumborum of rabbits and the associations among such effects with microbial spoilage, myoglobin autoxidation, lipid oxidation, and protein oxidation were investigated. The total aerobic count, total volatile basic nitrogen content, metmyoglobin content, protein carbonyl content, and contents of thiobarbituric acid-reactive substances steadily increased during retail display. Moreover, the lightness and redness of the rabbit meat significantly (P < 0.05) declined over time, whereas the yellowness increased considerably (P < 0.05) with prolonged retail time. Canonical correlation analysis suggested that microbial spoilage, myoglobin autoxidation, lipid oxidation, and protein oxidation jointly affected rabbit meat color. Linear mixed models further revealed that microbial spoilage, myoglobin autoxidation, lipid oxidation and protein oxidation positively affected yellowness, and they inversely impacted lightness and redness.

Image Analysis and Computer Vision Applications in Animal Sciences: An Overview

Computer Vision, Digital Image Processing, and Digital Image Analysis can be viewed as an amalgam of terms that very often are used to describe similar processes. Most of this confusion arises because these are interconnected fields that emerged with the development of digital image acquisition. Thus, there is a need to understand the connection between these fields, how a digital image is formed, and the differences regarding the many sensors available, each best suited for different applications. From the advent of the charge-coupled devices demarking the birth of digital imaging, the field has advanced quite fast. Sensors have evolved from grayscale to color with increasingly higher resolution and better performance. Also, many other sensors have appeared, such as infrared cameras, stereo imaging, time of flight sensors, satellite, and hyperspectral imaging. There are also images generated by other signals, such as sound (ultrasound scanners and sonars) and radiation (standard x-ray and computed tomography), which are widely used to produce medical images. In animal and veterinary sciences, these sensors have been used in many applications, mostly under experimental conditions and with just some applications yet developed on commercial farms. Such applications can range from the assessment of beef cuts composition to live animal identification, tracking, behavior monitoring, and measurement of phenotypes of interest, such as body weight, condition score, and gait. Computer vision systems (CVS) have the potential to be used in precision livestock farming and high-throughput phenotyping applications. We believe that the constant measurement of traits through CVS can reduce management costs and optimize decision-making in livestock operations, in addition to opening new possibilities in selective breeding. Applications of CSV are currently a growing research area and there are already commercial products available. However, there are still challenges that demand research for the successful development of autonomous solutions capable of delivering critical information. This review intends to present significant developments that have been made in CVS applications in animal and veterinary sciences and to highlight areas in which further research is still needed before full deployment of CVS in breeding programs and commercial farms.

Digital Image Filtering Optimization Supporting Iberian Ham Quality Prediction

Digital images of food for later analysis tend to be heterogeneous in terms of color and luminosity. Improving these images by using filters is necessary and crucial before further processing. This paper compares the non-use of filters and the use of high-pass filters in the images of hand-cut Iberian ham that will be used in a multifractal analysis for the study of fat and its infiltration. The yielded results show that with the use of a high-pass filter, more accurate fractal dimensions were obtained, which can be featured in predictive techniques of Iberian ham quality.

Meat quality evaluation based on computer vision technique: A review

Nowadays people tend to include more meat in their diet thanks to the improvement in standards of living as well as an increase in awareness of meat nutritive values. To ensure public health, therefore, there is a need for a rise in worldwide meat production and consumption. Further attention is also required as to how the safety and the quality of meat production process should be assessed. Classical methods of meat quality assessment, however, have some disadvantages; expensive and time-consuming. This study intends to introduce an alternative method known as Computer Vision (CV) for the assessment of various quality parameters of muscle foods. CV has several advantages over the traditional methods. It is non-destructive, easy, and quick, hence, more efficient in meat quality assessments. This study aims to investigate different quality characteristics of some muscle foods using CV. It closes with a discussion on the future challenges and expected opportunities of the practical application of CV in the meat industry.

Quality Assessment of Pork and Turkey Hams Using FT-IR Spectroscopy, Colorimetric, and Image Analysis

The implementation of fast and nondestructive methods in meat products and colds cuts have become increasingly important to evaluate their quality in relation to different factors such as origin, type of processing, freshness, adulteration, and authenticity. In this study, Fourier Transform Infrared Spectroscopy (FT-IR), colorimetric, and image-analysis methods were implemented to characterize and classify ham cold cuts in terms of meat type, processing, and shelf life during refrigerated storage. Two types of commercial hams (made from pork and turkey) and three types of processing (boiled, smoked, and roasted) were selected. By using the most appropriate color parameters, a*, h, and C*, as well as the textural features' angular second moment, long-running emphasis, and standard deviation of image intensity from the hams' images, high-classification values for the different ham samples were achieved. The FT-IR analysis revealed the presence of absorbance bands of proteins, triglycerides, fatty acids, and carbohydrates with different intensities according to meat type and processing. Refrigeration storage caused significant alterations of color parameters and a partial degradation of triglycerides and proteins. Moreover, the image-analysis findings indicated that storage period caused significant degradation of ham images relating to local linearity, and structural and textural continuum.

Prediction of pork quality parameters by applying fractals and data mining on MRI

This work firstly investigates the use of MRI, fractal algorithms and data mining techniques to determine pork quality parameters non-destructively. The main objective was to evaluate the capability of fractal algorithms (Classical Fractal algorithm, CFA; Fractal Texture Algorithm, FTA and One Point Fractal Texture Algorithm, OPFTA) to analyse MRI in order to predict quality parameters of loin. In addition, the effect of the sequence acquisition of MRI (Gradient echo, GE; Spin echo, SE and Turbo 3D, T3D) and the predictive technique of data mining (Isotonic regression, IR and Multiple linear regression, MLR) were analysed. Both fractal algorithm, FTA and OPFTA are appropriate to analyse MRI of loins. The sequence acquisition, the fractal algorithm and the data mining technique seems to influence on the prediction results. For most physico-chemical parameters, prediction equations with moderate to excellent correlation coefficients were achieved by using the following combinations of acquisition sequences of MRI, fractal algorithms and data mining techniques: SE-FTA-MLR, SE-OPFTA-IR, GE-OPFTA-MLR, SE-OPFTA-MLR, with the last one offering the best prediction results. Thus, SE-OPFTA-MLR could be proposed as an alternative technique to determine physico-chemical traits of fresh and dry-cured loins in a non-destructive way with high accuracy.

Evaluation of fat grains in gothaj sausage using image analysis

Fat is an irreplacable ingredient in the production of sausages and it determines the appearance of the resulting cut to a significant extent. When shopping, consumers choose a traditional product mostly according to its appearance, based onwhat they are used to. Chemical analysis is capable to determine the total fat content in the product, but it cannot accurately describe the shape and size of fat grains which the consumer observes when looking at the product. The size of fat grains considered acceptable by consumers can be determined using sensory analysis or image analysis. In recent years, image analysis has become widely used when examining meat and meat products. Compared to the human eye, image analysis using a computer system is highly effective, since a correctly adjusted computer program is able to evaluate results with lower error rate. The most commonly monitored parameter in meat products is the aforementioned fat. The fat is located in the cut surface of the product in the form of dispersed particles which can be fairly reliably identified based on color differences in the individual parts of the product matrix. The size of the fat grains depends on the input raw material used as well as on the production technology. The present article describes the application of image analysis when evaluating fat grains in the appearance of cut of the Gothaj sausage whose sensory requirements are set by Czech legislation, namely by Decree No. 326/2001 Coll., as amended. The paper evaluates the size of fat mosaic grains in Gothaj sausages from different manufacturers. Fat grains were divided into ten size classes according to various size limits; specifically, 0.25, 0.5, 0.75, 1.0, 1.5, 2.0, 2.5, 5.0, 8.0 and over 8 mm. The upper limit of up to 8 mm in diameter was chosen based on the limit for the size of individual fat grains set by the legislation. This upper limit was not exceeded by any of the products. On the other side the mosaic had the hightest representation of 0.25 mm fat grains.